Abstract:

Methods, devices, and systems that aggregate and retrieve network sensor
data. In one approach, an exemplary method includes receiving a plurality
of instances of correlated sensor data from a plurality of remote data
storages, each instance of sensor data respectively correlating to an
occurrence. In another approach, an exemplary method includes receiving
from an input-selector an input selection corresponding to a
target-occurrence having at least one representative feature, and
searching stored sensor data for sensor data correlating to the
target-occurrence.

Claims:

1-21. (canceled)

22. An occurrence-data archival system, comprising:a computing device
operable to communicate with a local data storage and one or more remote
data storages; andinstructions that cause the computing device to perform
a process including:receiving a plurality of instances of correlated mote
data from the one or more remote data storages, each instance of mote
data respectively correlating to an occurrence; andstoring the received
plurality of instances of correlated mote data in the archival data
storage.

23. The system of claim 22, further including a local data storage device.

24. The system of claim 23, wherein the local data storage device is
operable as an archival data storage.

25. The system of claim 22, wherein the correlating to an occurrence
includes correlating to at least one representative feature of the
occurrence.

26. The system of claim 25, wherein the at least one representative
feature includes a sequence.

27. The system of claim 22, wherein the occurrence includes a real world
event.

28. The system of claim 22, wherein the occurrence includes a real world
incident.

29. The system of claim 22, wherein the plurality of instances of mote
data include a first instance of mote data correlating to a respective
first occurrence and a second instance of mote data correlating to a
respective second occurrence.

30. The system of claim 29, wherein the mote data of the first occurrence
relates a first matter and the mote data of the second occurrence relates
to a second matter.

31. The system of claim 30, wherein the first and second matters are
substantially similar.

32. The system of claim 22, wherein each instance of mote data includes a
respective sequence.

33. The system of claim 32, wherein the sequence includes a chronological
sequence.

34. The system of claim 22, wherein each instance of mote data was
acquired by at least one mote of a plurality of distributed motes.

35. The system of claim 34, wherein each mote is part of a network of
motes.

36. The system of claim 22, wherein at least one instance of the mote data
was acquired by a mote operable to sense at least one parameter.

37. The system of claim 22, further including (c) an information security
measure operable to protect the plurality of instances of mote data
stored in the local data storage.

38. The system of claim 37, wherein the information security measure
protects by providing at least one item selected from a group consisting
of information confidentiality, information integrity, and access
control.

39. The system of claim 22, wherein the process includes deleting a
portion of the stored data from the at least one remote data storage.

40. The system of claim 22, wherein at least one instance of the received
mote data includes an associated tentative occurrence-identifier.

41. The system of claim 40, wherein the tentative occurrence-identifier is
associated by a method including:receiving an input selection
corresponding to a target-occurrence having at least one representative
feature;automatically selecting a pattern recognition criteria
corresponding to at least one representative feature of the
target-occurrence;searching mote data for data correlating to the
target-occurrence using the selected pattern recognition criteria; andif
mote data correlating to the target-occurrence representative feature is
found, associating the tentative occurrence-identifier with the
correlating mote data.

42-111. (canceled)

112. An occurrence-data archival system, comprising:a computing device
operable to communicate with an archival data storage and remote data
storage devices; andinstructions that cause the computing device to
perform a process, including:receiving one or more mote data sets from at
least one of the remote data storage devices, each mote data set
representing a respective feature sensed by its respective remote storage
device, the respective feature having a correlation to a reference;
andstoring the one or more mote data sets in the archival data storage.

113. The system of claim 112, wherein the reference of a first data set is
a first occurrence.

114. The system of claim 112, wherein the reference of a second data set
is a second occurrence.

115. The system of claim 114, wherein the first occurrence and the second
occurrence are at least substantially the same.

116. A method implemented in a computing device, the method
comprising:receiving a plurality of mote data sets from at least one
remote data storage device, each sensor data set representing a
respective feature sensed by its respective remote storage device, the
respective feature having a correlation to a reference;storing the
received plurality of mote data sets in a local data storage device;
andapplying an information security measure to the stored plurality of
mote data sets.

117. The method of claim 116, wherein the reference of a first data set is
a first occurrence.

118. The method of claim 117, wherein the first reference is a temporal
reference related to the first occurrence.

119. The method of claim 116, wherein the reference of a second data set
is a second occurrence.

120. The method of claim 119, wherein the second reference is a temporal
reference related to the second occurrence.

121. The method of claim 119, wherein the first occurrence and the second
occurrence are at least substantially the same.

122. The method of claim 116, further including protecting the received
plurality of instances of correlated mote data with an information
security measure.

123. (canceled)

124. A machine-implemented method, comprising:receiving a
target-occurrence having at least one representative
feature;automatically searching one or more instances of mote data for
mote data correlating to the target-occurrence, each instance of mote
data corresponding to an occurrence; andproviding the mote data
correlating to the target-occurrence.

125. The method of claim 124, wherein automatically searching one or more
instances of mote data for mote data correlating to the target-occurrence
includes correlating to at least one representative feature of the
occurrence.

126. The method of claim 124, wherein automatically searching one or more
instances of mote data includes automatically searching one or more
instances of mote data, wherein at least one instance of mote data
corresponds to an occurrence that includes a real world event.

127. The method of claim 124, wherein automatically searching one or more
instances of mote data includes automatically searching one or more
instances of mote data, wherein at least one instance of mote data
corresponds to an occurrence that includes a real world incident.

128. The method of claim 124, wherein automatically searching one or more
instances of mote data includes automatically searching one or more
instances of mote data, wherein at least one instance of mote data
corresponds to an occurrence that includes a change in a data sequence.

129. The method of claim 124, wherein automatically searching one or more
instances of mote data includes automatically searching one or more
instances of mote data, wherein at least one instance of mote data
corresponds to an occurrence that includes a change in a time domain.

130. The method of claim 124, wherein automatically searching one or more
instances of mote data includes automatically searching a first instance
of mote data correlating to a respective first occurrence and a second
instance of mote data correlating to a respective second occurrence.

131. The method of claim 124, wherein automatically searching one or more
instances of mote data includes automatically searching a first instance
of mote data correlating to a first matter and a second instance of mote
data correlating to a second matter.

132. The method of claim 124, wherein receiving a target-occurrence having
at least one representative feature includes receiving a
target-occurrence having at least one frequency characteristic.

133. The method of claim 124, wherein receiving a target-occurrence having
at least one representative feature includes receiving a
target-occurrence having at least one acoustic frequency characteristic.

134. The method of claim 124, wherein receiving a target-occurrence having
at least one representative feature includes receiving a
target-occurrence having at least one acoustic signature of a known
event.

135. The method of claim 124, wherein receiving a target-occurrence having
at least one representative feature includes receiving a
target-occurrence having at least one light frequency characteristic.

136. The method of claim 124, wherein receiving a target-occurrence having
at least one representative feature includes receiving a
target-occurrence having at least one amplitude characteristic.

137. The method of claim 124, wherein receiving a target-occurrence having
at least one representative feature includes receiving a
target-occurrence having a frequency characteristic and an amplitude
characteristic.

138. The method of claim 124, wherein receiving a target-occurrence having
at least one representative feature includes receiving a
target-occurrence having at least one magnetic characteristic.

139. The method of claim 124, wherein receiving a target-occurrence having
at least one representative feature includes receiving a
target-occurrence having at least one repetitive sequence.

140. The method of claim 124, wherein receiving a target-occurrence having
at least one representative feature includes receiving a
target-occurrence having at least one chronological sequence.

Description:

CROSS REFERENCE TO RELATED APPLICATIONS

[0001]The present application is related to and claims the benefit of the
earliest available effective filing date(s) from the following listed
application(s) (the "Related Applications") (e.g., claims earliest
available priority dates for other than provisional patent applications
or claims benefits under 35 USC § 119(e) for provisional patent
applications, and incorporates by reference in its entirety all subject
matter of the following listed application(s); the present application
also claims the earliest available effective filing date(s) from, and
also incorporates by reference in its entirety all subject matter of any
and all parent, grandparent, great-grandparent, etc. applications of the
following listed application(s) (in the event of any inconsistencies
between the instant application and an application incorporated by
reference, the instant application controls):

RELATED APPLICATIONS

[0002]For purposes of the United States Patent and Trademark Office
(USPTO) extra-statutory requirements (described more fully below), the
present application is:

[0024]The United States Patent and Trademark Office (USPTO) has published
a notice to the effect that the USPTO's computer programs require that
patent applicants reference both a serial number and indicate whether an
application is a continuation or continuation in part. Stephen G. Kunin,
Benefit of Prior-Filed Application, USPTO Electronic Official Gazette,
Mar. 18, 2003. The present applicant entity has provided a specific
reference to the application(s) from which priority is being claimed as
recited by statute. Applicant entity understands that the statute is
unambiguous in its specific reference language and does not require
either a serial number or any characterization such as "continuation" or
"continuation-in-part." Notwithstanding the foregoing, applicant entity
understands that the USPTO's computer programs have certain data entry
requirements, and hence applicant entity is designating the present
application as a continuation in part of its parent applications, but
expressly points out that such designations are not to be construed in
any way as any type of commentary and/or admission as to whether or not
the present application contains any new matter in addition to the matter
of its parent application(s).

BACKGROUND

[0025]The present era of computing has introduced an array of small
devices that perform a variety of specific functions. Cellular phones,
pagers and portable digital assistants are common examples of these. As
technology progresses, however, devices will continue to become smaller
and more specialized. One class of small device that is beginning to
emerge is a tiny, sensor, sometimes known as a "mote" that is often
implemented in a networked configuration.

[0026]Sensor nodes, sometimes referred to as sensor devices, are
undergoing significant advances in structure and low power technology. In
some applications, sensor nodes may utilize micro-electromechanical
systems, or MEMS, technology. Sensor nodes may include more than one
component, such as an embedded processor, digital storage, power source,
a transceiver, and an array of sensors, environmental detectors, and/or
actuators. In some cases, sensor nodes may rely on small batteries,
solar-powered cell, or ambient energy for power, and run for long periods
of time without maintenance.

[0027]Communication characteristics of nodes may be determined by physical
design characteristics and intended use scenarios or both. In some
applications, sensor nodes may act as a data source, and it may also
forward data from other sensors that are out of range of a central
station.

[0028]The practical applications of such mini-devices range from
environmental monitoring to micro-robots capable of performing
microscopic scale tasks. While functionality of an individual sensor node
may be limited, a grouping of nodes working together can accomplish a
range of tasks, including high level tasks. The tasks of a grouping may
include operations such as general information gathering, security,
industrial monitoring, military reconnaissance, or biomedical monitoring.

[0029]The integration of computation, storage, communication, and physical
interaction in silicon has shrunk some sensor nodes down to microscopic
scales. The ability to create sensors and actuators with IC technology
and integrate them with computational logic has created an abundance of
low power, tiny sensor nodes. Combining these tiny sensor nodes with low
power wireless communication networks aids in developing economical,
distributed sensors networks. The number of sensor nodes used in a
network is increasing as their cost decreases and functionality
increases. As a result, the sheer volume of data created by sensor
networks, particularly distributed sensor networks, is rapidly
increasing.

SUMMARY

[0030]An embodiment provides a data system. The system includes a
computing device operable to communicate with a local data storage device
and with a remote data storage operable to store a plurality of instances
of sensor data. Each instance of sensor data respectively correlating to
an occurrence, and instructions, which when implemented in a computing
device, cause the computing device to perform steps. The steps include
receive the plurality of instances of sensor data from the remote digital
storage device, store the received plurality of instances of correlated
sensor data in the local data storage device, and receive an input
selection corresponding to a target-occurrence having at least one
representative feature. Also, automatically search the stored plurality
of instances of sensor data for sensor data correlating to the
target-occurrence. If sensor data correlating to the target-occurrence is
found, provide the correlating sensor data. The search for sensor data
correlating to the target-occurrence may include correlating to at least
one representative feature of the occurrence. An occurrence may include a
real world event, a real world incident, a change in a data sequence, and
a change in a time domain. The plurality of instances of sensor data may
include a first instance of sensor data correlating to a respective first
occurrence and a second instance of sensor data correlating to a
respective second occurrence. The first and second occurrences may have
happened at a substantially a same time, and/or at a substantially a same
location. The first and second occurrences may include an at least
substantially similar representative feature. The sensor data of the
first occurrence may relate a first matter and the sensor data of the
second occurrence may relate to a second matter. The first and second
matters may be substantially similar. Each instance of sensor data may
include a respective sequence, and the sequence may include a
chronological sequence.

[0031]Another embodiment provides an occurrence-data archival system. The
system includes a computing device operable to communicate with an
archival data storage and a plurality of remote data storages, and
instructions, which when implemented in a computing device, cause the
computing device to perform steps. The steps include receive a plurality
of instances of correlated sensor data from the plurality of remote data
storages, each instance of sensor data respectively correlating to an
occurrence, and store the received plurality of instances of correlated
sensor data in the archival data storage. The system may further include
a local data storage device operable as the archival data storage. The
correlating to an occurrence may include correlating to at least one
representative feature of the occurrence. The at least one representative
feature may include a sequence. An occurrence may include a real world
event, and a real world incident. The plurality of instances of sensor
data may include a first instance of sensor data correlating to a
respective first occurrence and a second instance of sensor data
correlating to a respective second occurrence. The sensor data of the
first occurrence may relate a first matter and the sensor data of the
second occurrence may relate to a second matter, and the first and second
matters may be at least substantially similar. Each instance of sensor
data may include a respective sequence, and the sequence may include a
chronological sequence. Each instance of sensor data may have been
acquired by at least one sensor node of a plurality of distributed sensor
nodes. Each sensor node may be part of a network of sensor nodes. The
system may further include an information security measure operable to
protect the stored plurality of instances of sensor data. The information
security measure may provide at least one selected from a group
consisting of information confidentiality, information integrity, and
access control. The instructions may further include delete a portion of
the stored data from the at least one remote data storage.

[0032]At least one instance of the received sensor data may include an
associated tentative occurrence-identifier. The tentative
occurrence-identifier may be associated by a method. The method includes
receiving an input selection corresponding to a target-occurrence having
at least one representative feature, and automatically selecting a
pattern recognition criteria corresponding to at least one representative
feature of the target-occurrence. Also, searching sensor data for data
correlating to the target-occurrence using the selected pattern
recognition criteria, and, if sensor data correlating to the
target-occurrence representative feature is found, associating the
tentative occurrence-identifier with the correlating sensor data.

[0033]A yet further embodiment provides a method implemented in a
computing device. The method includes receiving a plurality of instances
of correlated sensor data from a plurality of remote data storages, each
instance of sensor data respectively correlating to an occurrence. Also,
storing the received plurality of instances of correlated sensor data in
a local data storage device, and protecting the received plurality of
instances of correlated sensor data with an information security measure.
The receiving step further may include receiving the plurality of
instances of sensor data in response to a data push. The method may
further include requesting the plurality of instances of sensor data from
the remote data storage. The information security measure may protect by
providing at least one selected from a group consisting of information
confidentiality, information integrity, and access control.

[0034]An occurrence may include a real world event, a real world incident,
a change in a data sequence, and a change in a time domain. The plurality
of instances of sensor data may include a first instance of sensor data
correlating to a respective first occurrence and a second instance of
sensor data correlating to a respective second occurrence. The first and
second occurrences may have happened at substantially a same time, and/or
at substantially a same location. The first and second occurrences may
include an at least substantially similar representative feature. The
sensor data of the first occurrence may relate a first matter and the
sensor data of the second occurrence may relate to a second matter. The
first and second matters may be substantially similar. Each instance of
sensor data may include a respective sequence, and the sequence may
include a chronological sequence. An occurrence may include a sequence, a
representative feature, and an associated tentative
occurrence-identifier. The method may further include deleting at least
one of the plurality of instances of sensor data from its respective
remote data storage.

[0035]An embodiment includes an occurrence-data retrieval system. The
system includes a computing device operable to communicate with a data
storage which is operable to store a plurality of instances of sensor
data acquired from at least one sensor node of a sensor network, each
instance of sensor data respectively correlating to an occurrence, and
instructions, which when implemented in a computing device, cause the
computing device to perform steps. The steps include receive from an
input-selector an input selection corresponding to a target-occurrence,
and automatically search the plurality of instances of sensor data stored
in the data storage for sensor data correlating to the target-occurrence.
If sensor data correlating to the target-occurrence is found, provide the
correlating sensor data. Each occurrence may include a representative
feature. The data storage may be remote or local to the computing device.
The computing device may communicate with a remote data storage through
an intermediate computing device. The provide instruction further may
include provide the correlating sensor data to the input-selector, and/or
a third party. The instructions further include receive the plurality of
instances of sensor data from at least one remote data storage, and store
the received plurality of instances of correlated sensor data in a local
data storage.

[0036]Another embodiment provides an occurrence-data retrieval system. The
system includes a local digital data storage device operable to store a
plurality of instances of sensor data, each instance of sensor data
correlating to at least one representative feature of an occurrence. The
system further includes a central computing device operable to
communicate with the local digital data storage device, and instructions,
which when implemented in a computing device, cause the computing device
to perform steps. The steps include receive from an input-selector an
input selection corresponding to a target-occurrence having at least one
representative feature, and automatically select a pattern recognition
criteria corresponding to at least one representative feature of the
target-occurrence. In response to the input selection corresponding to
the target-occurrence, automatically search the plurality of instances of
stored sensor data for data correlating to the target-occurrence using
the selected pattern recognition criteria. If sensor data correlating to
the target-occurrence representative feature is found, provide the
correlating sensor data. An input-selector may include a machine, and a
user. The pattern recognition criteria may be automatically selected by
the instructions in response to the target-occurrence. The input
selection further may include a selection of a representative feature of
the target-occurrence. The pattern recognition criteria may be
automatically selected by the instructions in response to the
input-selected representative feature. The automatic search instruction
may include using the pattern recognition criteria selected in response
to the inputted representative feature. The instructions may further
include protect the plurality of instances of sensor data stored in the
at least one local digital data storage device from unauthorized access.

[0037]A yet further embodiment provides an occurrence-data retrieval
system. The system includes a computing device operable to communicate
with a data storage device having a plurality of instances of sensor data
stored thereon, each instance of sensor data correlating to an
occurrence, and an information security measure protecting the sensor
data stored in the at least one selected data storage device from
unauthorized access. The system also includes instructions, which when
implemented in a computing device, cause the computing device to perform
steps. The steps include receive from an input-selector an input
selection corresponding to a target-occurrence having at least one
representative feature, a recipient selection, and a tendered access
authorization. In response to the tendered access authorization,
determine if at least one of the input-selector and recipient have an
access right, and automatically select a pattern recognition criteria
corresponding to at least one representative feature of the
target-occurrence. In response to the input selection corresponding to
the target-occurrence, automatically search the plurality of instances of
sensor data stored in the data storage device for data correlating to the
target-occurrence using the selected pattern recognition criteria. If
sensor data correlating to the target-occurrence representative feature
is found, and if at least one of the source and recipient have an access
right, provide the correlating sensor data to the recipient. An
input-selector may include a machine and a user. The input-selector and
the recipient may be a same party. A recipient may be a user, and a
machine. The data storage device may include a device selected from a
group consisting of a local data storage device and a remote data storage
device.

[0038]An embodiment provides an occurrence-data retrieval system. The
system includes a data storage device having a stored plurality of
instances of sensor data, each instance of sensor data correlating to at
least one representative feature of an occurrence and including an
associated tentative occurrence-identifier. Also, a computing device
operable to communicate with at the least one data storage device, and an
information security measure that controls access to the stored plurality
of instances of sensor data. Instructions, which when implemented in a
computing device, cause the computing device to perform steps. The steps
include receive from an input-selector an input selection corresponding
to a target tentative occurrence-identifier, and a tender of access
authorization. In response to the input selection corresponding to the
target tentative occurrence-identifier, automatically search the
plurality of instances of sensor data for data correlating to the target
tentative occurrence-identifier. If sensor data correlating to the target
tentative occurrence-identifier is found, and if at least one of the
source and recipient possess an access right, provide the correlating
sensor data. The data storage device may include a device selected from a
group consisting of a local data storage device and a remote data storage
device.

[0039]A further embodiment provides a method. The method includes
receiving from an input-selector an input selection corresponding to a
target-occurrence having at least one representative feature. The method
also includes automatically searching a plurality of instances of stored
sensor data for sensor data correlating to the target-occurrence, each
instance of sensor data correlating to an occurrence having at least one
representative feature. If sensor data correlating to the
target-occurrence is found, providing the correlating sensor data. The
plurality of instances of sensor data may be stored in a data storage
device. The data storage device may be selected from a group consisting
of a data storage device local to the computing device and a data storage
device remote to the computing device. The providing further may include
indicating a rank for at least two instances of the correlating sensor
data in a hierarchy of the found correlating sensor data.

[0040]A yet further embodiment provides a method implemented in a
computing device. The method includes receiving an input selection from
an input-selector corresponding to a target-occurrence having at least
one representative feature, a recipient selection, and a tendered access
authorization. The method further includes automatically selecting a
pattern recognition criteria corresponding to at least one representative
feature of the target-occurrence. In response to the input selection
corresponding to the target-occurrence, automatically searching a
plurality of instances of stored correlated sensor data for data
correlating to the target-occurrence using the selected pattern
recognition criteria, each instance of sensor data correlating to at
least one representative feature of an occurrence. In response to the
tendered access authorization, determine if at least one of the
input-selector and the recipient posses an access right. If sensor data
correlating to the target-occurrence representative feature is found, and
if at least one of the input-selector and the recipient have an access
right, providing the correlating sensor data. The instances of stored
correlated sensor data may be stored in a data storage device local to
the computing device.

[0041]An embodiment provides a method implemented in a computing device.
The method includes receiving an input selection from an input-selector
corresponding to a tentative target-occurrence identifier, the
target-occurrence having at least one representative feature, a recipient
selection, and a tendered access authorization. In response to the input
selection corresponding to the tentative target-occurrence identifier,
automatically searching a plurality of instances of stored correlated
sensor data for data correlating to the tentative target-occurrence
identifier. If sensor data correlating to the tentative target-occurrence
identifier is found, providing the correlating sensor data. The instances
of stored correlated sensor data may be stored in a data storage device
local to the computing device.

[0042]Another embodiment provides an occurrence-data archival system. The
system includes a computing device operable to communicate with an
archival data storage and a plurality of remote data storage devices, and
instructions, which when implemented in a computing device, cause the
computing device to perform steps. A step includes receive a plurality of
sensor data sets from remote data storage devices, each sensor data set
representing a respective feature sensed by its respective remote storage
device, the respective feature having a correlation to a reference.
Another step includes store the received plurality of sensor data sets in
the archival data storage. The reference of a first data set may be a
first occurrence. The reference of a second data set may be a second
occurrence. The first occurrence and the second occurrence may be at
least substantially the same.

[0043]A yet further embodiment provides a method implemented in a
computing device. The method includes receiving a plurality of sensor
data sets from remote data storage devices, each sensor data set
representing a respective feature sensed by its respective remote storage
device, the respective feature having a correlation to a reference. The
method further includes storing the received plurality of sensor data
sets in a local data storage device. The reference of a first data set
may be a first occurrence, which may be a temporal reference related to
the first occurrence. The reference of a second data set may be a second
occurrence, which may be a temporal reference related to the second
occurrence. The first occurrence and the second occurrence may be at
least substantially the same. The method may further include protecting
the received plurality of instances of correlated sensor data with an
information security measure.

[0044]An embodiment provides a data retrieval system. The system includes
a local data storage device operable to store sensor data sets from
remote data storage devices, each sensor data set representing a
respective feature sensed by its respective remote storage device, the
respective feature having a correlation to a reference. The system also
includes a computing device operable to communicate with a local data
storage device, and instructions, which when implemented in a computing
device, cause the computing device to perform steps. The steps include
receive from an input-selector an input selection corresponding to a
target-reference, and automatically search the instances of sensor data
stored in the data storage device for sensor data correlating to the
target-reference. If sensor data correlating to the target-reference is
found, provide the correlating sensor data.

[0045]A further embodiment provides a method. The method includes moving a
portable central computing device to location such that the portable
central computing device communicates with at least one sensor node of a
network of distributed sensor nodes. Each sensor node is operable to
sense and store a plurality of data sets each respectively representing a
respective feature, each respective feature having a correlation to an
occurrence. The method also includes causing the central computing device
to acquire at least one sensor data set. The method may include selecting
through the central computing device a sensor data set to be transmitted
from the at least one sensor node to the central computing device. The
method may include confirming that the at least one selected sensor data
set is acquired by the central computing device. Each sensor node may
include operability to filter the plurality of data sets for a feature
correlating to a target-occurrence. Causing the central computing device
to acquire at least one data set may include causing the central
computing device to acquire at least one data set having a feature
correlating to the target occurrence.

[0046]These and various other features as well as advantages of the
present invention will be apparent from a reading of the following
detailed description and a review of the associated drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0047]Aspects of the invention, together with features and advantages
thereof, may be understood by making reference to the following
description taken in conjunction with the accompanying drawings, in the
several figures of which like referenced numerals identify like elements,
and wherein:

[0048]FIG. 1 illustrates a sensor node, or "mote;"

[0049]FIG. 2 illustrates a graph of a hypothetical data related to a
sensed parameter that may define an occurrence;

[0050]FIG. 3 is a table illustrating several classes of occurrences, a
relationship between an individual occurrence and at least one
characteristic or attribute of the individual occurrences, and
representative features of the individual characteristics;

[0051]FIG. 4 illustrates a distributed sensor network;

[0052]FIGS. 5A and 5B include a flow diagram illustrating an exemplary
process in which sensor data correlating to a target-occurrence is
acquired from a sensor network and stored;

[0054]FIG. 7 is a flow diagram illustrating an exemplary process that
aggregates and stores a plurality of instances of correlated sensor data
in an occurrence-data archive;

[0055]FIG. 8 is a flow diagram that illustrates exemplary steps of a
process that searches and retrieves certain instances of stored
correlated sensor data from an occurrence-data archive;

[0056]FIG. 9 is a flow diagram illustrating exemplary steps of a process
that searches a plurality of instances of occurrence data stored in a
data vault or data lock box and provides an output;

[0057]FIG. 10 is a flow diagram illustrating exemplary steps of a process
providing the output of FIG. 9; and

[0058]FIG. 11 is a flow diagram illustrating exemplary steps of a process
that redacts a selected instance of occurrence data from the plurality of
instances of stored occurrence data described in conjunction with FIG. 9.

DETAILED DESCRIPTION

[0059]In the following detailed description of exemplary embodiments,
reference is made to the accompanying drawings, which form a part hereof.
The detailed description and the drawings illustrate specific exemplary
embodiments by which the invention may be practiced. Other embodiments
may be utilized, and other changes may be made, without departing from
the spirit or scope of the present invention.

[0060]Throughout the specification and claims, the following terms take
the meanings explicitly associated herein unless the context dictates
otherwise. The meaning of "a", "an", and "the" include plural references.
The meaning of "in" includes "in" and "on."

[0061]FIG. 1 illustrates a sensor node 20, or "mote," many of which can be
combined to form a sensor network. As used herein, the term "mote"
typically means a semi-autonomous computing, communication, and/or
sensing device as described in the mote literature (e.g., Intel
Corporation's mote literature), as well as equivalents recognized by
those having skill in the art (e.g., Intel Corporation's smart dust
projects). Mote 20 depicts a specific example of a more general mote.

[0062]The sensor node 20 may be of various sizes, and may be as small as a
quarter coin, or smaller, as sensor node sizes are now in the millimeter
range. The sensor node 20 includes a power source 22, a logic
circuit/microprocessor 24, a storage device 25, a transmitter (or
transceiver) 26, a communications coupler 28 coupled to the transmitter
26, and a sensor element 30. Alternatively, the mote may be unpowered or
passive, drawing its power from a reader or another source.

[0063]In the illustrated embodiment, the power source 22 provides power to
the sensor node 20. For example, the power source 22 may include a
battery, a solar-powered cell, and/or a continuous power supply furnished
by an external power source, such as by connection to a power line. By
way of example, the storage device 25 includes any computer readable
media, such as volatile and/or nonvolatile media, removable and/or
non-removable media, for storing computer data in permanent or
semi-permanent form, and can be implemented with any data storage
technology. Alternatively, the storage device 25 may store data in a form
that can be sampled or otherwise converted into a form storable in a
computer readable media.

[0064]The transmitter 26 transmits a data signal. In an optional
embodiment, the transmitter 26 both receives and transmits data signals
(transceiver). A "data signal" includes, for example and without
limitation, a current signal, voltage signal, magnetic signal, or optical
signal in a format capable of being stored, transferred, combined,
compared, or otherwise manipulated. The transmitter 26 may include
wireless, wired, infrared, optical, and/or other communications
techniques, for communication with a central computing device or central
station, and optionally other sensor nodes, using the communications
coupler 28. The communications coupler 28 may include an antenna for
wireless communication, a connection for wired connection, and/or an
optical port for optical communication.

[0065]The sensor node 20 may include any type of data processing capacity,
such a hardware logic circuit, for example an application specific
integrated circuit (ASIC) and a programmable logic, or such as a
computing device, for example, a microcomputer or microcontroller that
include a programmable microprocessor. The embodiment of the sensor node
20 illustrated in FIG. 1 includes data-processing capacity provided by
the microprocessor 24. The microprocessor 24 may include memory,
processing, interface resources, controllers, and counters. The
microprocessor 24 also generally includes one or more programs stored in
memory to operate the sensor node 20. If an embodiment uses a hardware
logic circuit, the logic circuit generally includes a logical structure
that operates the sensor node 20.

[0066]The sensor node 20 includes one or more sensor elements 30 that are
capable of detecting a parameter of an environment in which the sensor
node is located and outputting a data signal. The sensor element 30 may
detect at least one parameter from a group of optical, acoustic,
pressure, temperature, thermal, acceleration, magnetic, biological,
chemical, and motion parameters. The optical parameter may include at
least one from a group consisting of infrared, visible, and ultraviolet
light parameters. For example and without limitation, the sensor element
30 may include a photo sensor to detect a level or change in level of
light, a temperature sensor to detect temperature, an audio sensor to
detect sound, and/or a motion sensor to detect movement. The sensor
element 30 may include a digital image capture device, such as for
example and without limitation, a CCD or CMOS imager that captures data
related to infrared, visible, and/or ultraviolet light images.

[0067]Typically, the sensor node 20 automatically acquires data related to
a parameter of the sensor node environment, and transmits data to a
central computing device. For example, the sensor element 30 in a form of
an acoustic sensor may acquire sound levels and frequencies, and transmit
the data related to the levels and frequencies along with a time track
using the transmitter 26 and the communication coupler 28. The
acquisition may be on any basis, such as continuously, intermittently,
sporadically, occasionally, and upon request. In an alternative
embodiment, the time track may be provided elsewhere, such as a device
that receives the sensor data.

[0068]By way of further example and without limitation, the sensor element
30 in a form of an optical digital camera may periodically acquire visual
images, such as for example, once each second, and to transmit the data
related to visual images along with a time track. In another example, the
sensor element 30 in the form of a temperature sensor may detect
temperature changes in two-degree temperature intervals, and to transmit
each two-degree temperature change along with the time it occurred. Each
of the above examples illustrates a sequence, ranging from continuous for
acoustical detection to a per occurrence basis for two-degree temperature
changes.

[0069]The sensor element 30 may sense operational parameters of the sensor
node 20 itself, such as its battery/power level, or its radio signal
strength. Sensor data, including a data related to a sensed parameter, is
transmitted from the sensor node 20 in any signal form via the
transmitter 26 and the communications coupler 28, to a receiver. The
receiver may be, for example, another sensor node 20, a central computing
device, or any other data receiver. The sensor data may include a time
and/or date that the data related to a parameter was acquired.

[0070]The sensor node 20 may include a unique identifier, and is operable
to communicate the identifier in an association with its sensed
parameter. In an alternative embodiment, the sensor node 20 may include a
configuration that determines its location, for example, by a GPS system,
by triangulation relative to a known point, or by communication with
other sensor nodes. Alternatively, the location of the sensor node 20 may
be a known parameter established previously. Similarly, location
identification may be associated with data originated and/or forwarded by
the sensor node.

[0071]FIG. 2 illustrates a graph 50 of a hypothetical chronological
sequence 52 of a sensed parameter that may define an occurrence. The
sequence 52 illustrates a chronological sequence of a parameter that
might be outputted by a sensor node, and is plotted on the graph 50 with
time on an x-axis and amplitude on a y-axis. The sinusoidal sequence 52
includes several representative features. A first representative feature
is that the sequence 52 includes only two frequencies, A and B. A second
representative feature is that each frequency lasts for three cycles
before the sequence 52 changes to the other frequency. A third
representative feature is that the sequence 52 amplitude is generally the
same over the time T.

[0072]For example, assume that an individual user is seeking data
representative of a car accident. The car accident is the
target-occurrence. Further, assume that a characteristic of a car
accident is that an emergency vehicle may approach and/or be present at
the scene with its siren activated. Further, assume that it is known that
a "do-dah, do-dah, do-dah" type siren used by some emergency vehicles,
such as fire, ambulance, or police, generates sound or acoustic waves
that include the three features of the sequence 52. Also, assume that the
sequence 52 represents a chronological sequence output parameter by an
acoustic sensor, such as element 30 of the sensor node 20 of FIG. 1.
Application of one or more pattern recognition criteria that recognizes
the three above representative features of a sensor data that includes
the sequence 52 is likely to locate sensor data representative of the car
accident occurrence that involved a presence of siren. The sensor data
may be either from a single sensor node 20 or a plurality of sensor nodes
20.

[0073]By way of further example, if the occurrence of interest is passage
of an emergency vehicle siren through an intersection monitored by an
acoustic sensor, a fourth representative feature would be a Doppler shift
in the frequencies A and B on the passage of the vehicle. Expansion of
the pattern recognition criteria to include recognition of the fourth
feature is likely to locate sensor data representative of the passage of
the emergency vehicle. This example may be expanded where each
intersection in a portion of a city is individually monitored by
networked, distributed acoustic sensor nodes. Application of the expanded
pattern recognition criteria to the chronological sequences of acoustic
data outputted by the sensor nodes is expected to locate data
representative of the passage of the emergency vehicle through each
intersection, including a time of passage. Note that in this example, the
siren is a selected target-occurrence while in the above example, the
siren is a characteristic of the selected target-occurrence, the car
accident.

[0074]An occurrence includes anything that may be of interest, for
example, to a user, a computing device, or machine. An occurrence may be
or include, for example, a reference, an incident, an accident, an event,
a real world event, a change in a data sequence, and a change in a time
domain. An occurrence may be a high-level matter such as a car crash or a
riot, or a lesser-level matter, such as a siren or gun shot. This
detailed description uses certain events having a sequence of at least
one parameter that may be detected by a sensor element to describe
embodiments. However, the invention is not so limited.

[0075]FIG. 3 is a table illustrating several classes of occurrences, a
relationship between an individual occurrence and at least one
characteristic or attribute of the individual occurrences, and
representative features of the individual characteristics. Table of FIG.
3 illustrates an anticipated relationship between occurrences,
characteristics, and features.

[0076]For example, occurrence 1 of FIG. 3 is a car crash. A car crash
includes a plurality of characteristics or attributes, such as (a)
breaking glass, (b) impact noise, (c) tire screech, and (d) approach and
presence of emergency vehicles. Each of these characteristics has
representative features that can be sensed by one or more sensor nodes,
such as the sensor node 20. Characteristic or attribute (a), breaking
glass of occurrence 1, a car crash, is expected to include a
representative feature of sequential, high, and broadly distributed sound
frequencies that would be sensed by an acoustic sensor, such as the
sensor element 30 of FIG. 1. Characteristic (d), approach and presence of
emergency vehicles, is expected to include a representative feature of a
siren being sounded as an emergency vehicle approaches a car accident
scene. A more detailed example of representative features of a "do-dah,
do-dah" siren pattern is described in conjunction with FIG. 2 above.
Other types of emergency sirens are expected to have different
representative features.

[0077]By way of further example, a siren sound, which is a characteristic
of occurrence 1, may also be considered an occurrence, and is shown as
occurrence 2 of FIG. 3. FIG. 3 also includes examples of fire, armored
convey passage, and physical assault as high-level occurrences, and a gun
shot as a lesser-level occurrence.

[0078]As described above, each occurrence has certain known and/or
discoverable features or representative features. In FIG. 2, the graph 50
of the hypothetical chronological sequence 52 of a sensed parameter
illustrates three representative features that may correspond to an
occurrence.

[0079]One or more representative features are selected for recognition of
sensor data representative of an occurrence of interest, which is also
referred to as a target-occurrence. Representative features are features
that correspond to a characteristic of an occurrence and provide a data
representation of the occurrence. A representative feature may be
individually selected by an input-selector, or automatically selected.
Any suitable pattern recognition criteria, such as which may be expressed
in an algorithm, method and/or device, is used to identify one or more of
the selected representative features of a target-occurrence for
identification, location, retention, and/or retrieval of sensor data
corresponding to the target-occurrence. In certain embodiments, the
pattern recognition criteria are computer implemented. "Pattern
recognition criteria" as used in this specification may include anything
that recognizes, identifies, or establishes a correspondence with, one or
more representative features of an occurrence. While the fields of
pattern recognition and artificial intelligence are sometimes considered
as separate fields, or that one is a subfield of the other, pattern
recognition as used herein may include methods and/or devices sometimes
described as artificial intelligence. Further, pattern recognition may
include data or image processing and vision using fuzzy logic, artificial
neural networks, genetic algorithms, rough sets, and wavelets. Further, a
determination of which features are representative features of a
target-occurrence may also be determined using pattern recognition.

[0080]FIG. 4 illustrates a distributed sensor network 70 that includes an
array of sensor nodes 80, a central computing device 90, at least one
digital storage device, illustrated as a digital storage device 100, and
a plurality of communications links. The sensor nodes of the plurality of
sensor nodes 80 are similar to the sensor node 20 of FIG. 1. For purposes
of illustration, the sensor nodes are given reference numbers indicative
of their communications tier with respect to the central computing device
90. The first tier has reference numbers 82.1.1-82.1.N, and the second
tier has reference numbers 82.2.1-82.2.N. Additional tiers are not
numbered for clarity. Each sensor node in the array of sensor nodes 80
may sense a same parameter. Alternatively, a plurality sensor nodes of
the array of sensor nodes 80 may respectively sense different parameters.
For example, the sensor node 82.1.1 may respectively sense acoustical
pressure and sensor node 82.1.2 may respectively sense temperature. The
respective parameters sensed by the individual sensor nodes may be mixed
and matched in any manner to provide a desired parameter description of
the area in which the array of sensor nodes 80 are deployed.

[0081]In an embodiment, the individual sensor nodes of the plurality of
sensor nodes 80 of the sensor network 70 are typically distributed, that
is they are physically separated from each other. However, in certain
embodiments, sensor nodes that sense different parameters are grouped in
proximity to provide a more complete data related to a location. Further,
in an embodiment, the sensor nodes of the array of sensor nodes 80 are
distributed over a geographical area Such distributed sensors may include
sensing "real world" environmental parameters occurring in a locale of
each sensor, for example and without limitation, weather, car crashes,
and gunshots. In another embodiment, the sensor nodes of the array of
sensor nodes 80 are distributed in a manner to sense a parameter related
to a physical entity, such as, for example and without limitation,
individual pieces of a distributed equipment, such as traffic lights or
cell-phone transmission towers, or a locale, such as seats in a stadium.

[0082]An exemplary system implementing an embodiment includes a computing
device, illustrated in FIG. 4 as a central computing device 90. In its
most basic configuration, the computing device 90 typically includes at
least one central processing unit, storage, memory, and at least some
form of computer-readable media. Computer readable media can be any
available media that can be accessed by the computing device 90. By way
of example, and not limitation, computer-readable media might comprise
computer storage media and communication media.

[0083]Computer storage media includes volatile and nonvolatile, removable
and non-removable media implemented in any method or technology for
storage of data such as computer readable instructions, data structures,
program modules or other data. Computer storage media includes, but is
not limited to, RAM, ROM, EPROM, flash memory or other memory technology,
CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic
cassettes, magnetic tape, magnetic disk storage or other magnetic storage
devices, or any other medium that can be used to store the desired data
and that can be accessed by the computing system 90. The computer storage
media may be contained within a case or housing of the computing device
90, or may be external thereto.

[0084]Communication media typically embodies computer-readable
instructions, data structures, program modules or other data in a
modulated data signal such as a carrier wave or other transport mechanism
and includes any information and/or delivery media. The term "modulated
data signal" means a signal that has one or more of its characteristics
set or changed in such a manner as to encode information in the signal.
By way of example, and not limitation, communication media includes wired
media such as a wired network or direct-wired connection, and wireless
media such as acoustic, radio frequency, infrared, and other wireless
media. Combinations of any of the above should also be included within
the scope of computer-readable media. Computer-readable media may also be
referred to as computer program product.

[0085]The digital storage device 100 may be any form of a computer data
digital storage device that includes a computer storage media, including
the forms of computer storage media described above. The digital storage
device 100 may be a local digital storage device contained within a case
housing the computing device 90. Alternatively, the digital storage
device 100 may be a local and external digital storage device proximate
to the computing device 90, or remote to the computing device, and that
coupled to the computing device 90 in either case by a communications
link 99.

[0086]The computing device 90 also includes communications ports that
allow the computing device to communicate with other devices. More
specifically, the computing device 90 includes a port 97 for a wired
communication link, such as the wired communication link 102 providing
communications with at least one sensor node of the array of sensor nodes
80. The computing device 90 also includes a wireless transceiver or
receiver coupled with a communications coupler, such as the antenna 96,
for wireless communication over a link, such as the wireless
communication link 104. The wireless communications link 104 provides
wireless communications with at least one sensor node of the array of
sensors devices 80. The wireless communication link 104 may include an
acoustic, radio frequency, infrared and/or other wireless communication
link. The computing device 90 further includes a port 98 for wired,
wireless, and/or optical communication over a communication link 108 with
a network, such as a local area network, wide area network, and Internet.
Such networking environments are commonplace in offices, enterprise-wide
computer networks, intranets and the Internet. The communications link
may include an acoustic, radio frequency, infrared and other wireless
connection.

[0087]The computing device 90 may also have input device(s) 94, such as
keyboard, mouse, pen, voice input device, touch input device, etc. The
computing device 90 may further have output device(s) 92, such as a
display, speakers, printer, etc. may also be included. Additionally, the
computing device 90 may also have additional features and/or
functionality.

[0088]The computing device 90 may be implemented in any suitable physical
form, including a mainframe computer, a desktop personal computer, a
laptop personal computer, and a reduced-profile portable computing
device, such as a PDA or other handheld device.

[0089]Logical operation of certain embodiments may be implemented as a
sequence of computer implemented steps, instructions, or program modules
running on a computing system and/or as interconnected machine logic
circuits or circuit modules within the computing system. The
implementation is a matter of choice dependent on the performance
requirements of the computing system implementing and embodiment. In
light of this disclosure, it will be recognized by one skilled in the art
that the functions and operation of various embodiments disclosed may be
implemented in software, in firmware, in special purpose digital logic,
or any combination thereof without deviating from the spirit or scope of
the present invention.

[0090]FIGS. 5A and 5B include a flow diagram illustrating an exemplary
process 120 in which sensor data correlating to a target-event is
acquired from a sensor network and stored. In certain embodiments, the
process 120 is implemented in a central computer, such as the computing
device 90 of FIG. 4. In other embodiments, at least a portion of the
process 120 is implemented in a sensor node of an array of sensor nodes,
such as the sensor node array 80 of FIG. 4.

[0091]After a start block, the process 120 moves to block 122. At block,
122, a computing device, such as the central computing device 90,
continuously receives sensed data of at least one parameter from a sensor
node over a communications link. The sensor node may be any sensor node,
such as the sensor node 82.1.2, 82.1.N, or 82.2.2 of the array of sensor
nodes 80 of FIG. 4. The communications link may be any communications
link known in the art, for example and without limitation, an optical, a
wireless, and/or a wired link. For example, FIG. 4 illustrates the sensor
node 82.1.2 communicating over the wired communications link 102, and the
sensor node 82.1.N communicating over the wireless communications link
104. FIG. 4 also illustrates the sensor node 82.2.2 communicating over a
wireless link 106 with the sensor node 82.1.3, which then relays and
communicates the data from the sensor node 82.2.2 with the computing
device 90 over the wired communications link 102.

[0092]Optimally, the sensed data is transmitted at intervals and
aggregated into the data related to the sensed at least one parameter by
a receiving device. In an alternative embodiment, the sensed data may be
transmitted continuously by the sensor node. Furthermore, in another
embodiment, the sensed data may include continuously sampled data at a
predetermined sampling rate, such as a temperature reading captured
during the first minute of every five-minute interval, or such as a
digital image captured once each second.

[0093]At block 124, the received sensed data is continuously stored in a
storage device, such as the storage device 100, as first sensor data set.
In an alternative embodiment, the first data set includes a multi-element
data structure from which elements of the data related to the sensed at
least one parameter can be removed only in the same order in which they
were inserted into the data structure. In another alternative embodiment,
the first data set includes a multi-element data structure from which
elements can be removed based on factors other than order of insertion.

[0094]At block 126, an input selection is received from an input-selector
of a target-event having at least one representative feature. In a
certain embodiment, the input-selector includes a user, who inputs the
selection of the target-event using the user input device 94 of FIG. 4.
The user may select the target-event from a list of possible
target-events displayed on the user output device 92. The list for
example, may be similar to the list of occurrences of FIG. 3. In other
embodiments, the input-selector includes a machine, or a program running
on a computing device, such as the computing device 90.

[0095]In an embodiment, the input selection of the target-event may
include a selection an event that is directly of interest. For example, a
sound pattern of interest, such as the siren sound that is event 2 of
FIG. 3. In another embodiment, the input selection of the target-event
may be formulated in terms of a parameter that correlates to the event
that is directly of interest. For example, where the event of interest is
a fire, the input may be formulated in terms of a siren sound indicating
an approach or presence of emergency vehicles. The siren sound is
characteristic (a) of a fire, which is event 3 of FIG. 3.

[0096]In a further embodiment, the input selection of the target-event is
formulated in terms of weighing and/or comparing several instances of a
sensed data of at least one parameter from a plurality of sensor nodes to
determine which of the several instances provide a good representation of
the target-event. For example, the input selection may request the best
sensed data from six sensor, such as the best sensed data from six
sensors that heard a gun shot during a time period.

[0097]At block 128, a pattern recognition criteria corresponding to at
least one representative feature of the target-event is selected. In an
embodiment, the method includes at least one representative feature of
each possible target-event. The process automatically selects one or more
pattern recognition criteria for recognition of sensor data
representative of or corresponding to the target-event. In certain
embodiments, the pattern recognition criteria are included with the
process 120, or available to the process from another source. For
example, pattern recognition criteria may be associated locally with the
computing device 90, or available to it over a communications link, such
as the communications link 108. In a further embodiment, pattern
recognition criteria are provided to the computing device by the
input-selector in conjunction with the input of selection of the
target-event.

[0098]At block 132, in response to the input selection corresponding to
the target-event, the first sensor data set is automatically searched for
data correlating to the at least one target-event representative feature
using the selected pattern recognition criteria.

[0099]In a certain embodiment, the received input selection of the
target-event further includes a selection of a representative feature of
the target-event. The inputted selection of a target-event representative
feature may be any feature that the input-selector chooses for searching
sensor data. For example, the selected representative feature may include
a time period and acoustic frequency components. The acoustic frequency
components may include a selected frequency pattern, such as a recognized
word, set of words, breaking glass, dog bark, door opening, alarm,
threshold acoustic level, and voiceprint. The selected representative
feature may include a selected electromagnetic pattern, such as a visible
light, infrared light, ultraviolet light, and radar. In this embodiment,
at block 128, a pattern recognition criteria is automatically selected by
instructions in response to the selected representative feature. Further,
at block 132, the first sensor data set is automatically searched using
the pattern recognition criteria selected in response to the inputted
representative feature.

[0100]At decision block 134, a determination is made if sensor data
correlating to the at least one target-event representative feature was
found. If the sensor data is not found, the process branches to block
138. If the sensor data is found, the process branches to block 136. At
block 136, the instructions cause the computing device 90 to store the
correlated sensor data in a retained data storage. The retained data
storage may be at any location. For example and without limitation, the
retained data storage may be local to the central computing device 90,
such its removable or non-removable media; it may included in the digital
storage device 100; or it may be a remote digital storage device
associated with the computing device 90 over a communications link, such
as the communications link 108. In an embodiment, access to the retained
data storage is restricted to authorized users. After storage of the
correlated sensor data in a retained data storage, the process moves to
block 138.

[0101]In certain embodiments, in addition to storing the sensor data
correlating to at least one target-event representative feature, the
process includes storing a portion of the sensor data that was sensed
before the found target-event representative feature. In other
embodiments, the instructions include storing a portion of the sensor
data that was sensed after the found target-event representative feature.
In still other embodiments, the instructions include storing a portion of
the sensor data that was sensed both before and after the found
target-event representative feature. These embodiments allow data
occurring before and/or after the representative features to be saved.

[0102]In another embodiment, the process includes assigning a tentative
event-identifier to the correlated sensor data. For example, if the
target-event is a fire, and if a search of the first data set for data
correlating to at least one fire event representative feature finds
correlating sensor data, the process includes association of a tentative
event-identifier, such as "fire," with the correlated sensor data. The
trial-event identifier is associated with the stored correlated sensor
data at block 136.

[0103]At block 138, the data related to the sensed at least one parameter
is continuously deleted from the first data set according to a deletion
sequence. In an embodiment, the deletion sequence includes a
substantially first-in, first-out order. In another embodiment, the
deletion sequence includes a factor other than order of insertion into
the data set.

[0104]At block 142, the process returns to block 132 to search another
portion of the continuously received sensed data. The process continues
while the continuous sensed data is received. The instructions then move
to the stop block.

[0105]An embodiment provides a computer implemented process for searching
the data related to the sensed at least one parameter from the first data
set and storing correlated sensor data for both the target-event as
described above and another target-event before deletion of the data from
the first data set. In an alternative embodiment, another input selection
is received corresponding to another target-event having at least one
representative event feature. The input selection is received in a manner
substantially similar to block 126. In a manner substantially similar to
block 128, another pattern recognition criteria is automatically selected
corresponding to at least one of the representative features of the
selected another target-event.

[0106]In a manner substantially similar to block 132, in response to the
input selection corresponding to the another target-event, the first
sensor data set is automatically searched for data correlating to the at
least one target-event feature of the another target-event using the
selected pattern recognition criteria. In a manner substantially similar
to decision block 134, if sensor data correlating to the at least one
target-event representative feature of the another target-event is found,
the correlated sensor data is stored in the same retained data storage
used to store representative features of the first target-event, or
another retained data storage.

[0107]A further embodiment includes substantially simultaneously storing
correlated sensor data for the target-event from two sensor nodes, each
node generating separate data related to a same or a different sensed
parameter. In such an embodiment, two parallel instances of sensed
parameters are searched by the computing device 90 of FIG. 3 for data
correlating to at least one representative feature of the target-event.
In a manner substantially similar to block 122, data related to a sensed
parameter from a second sensor node of the plurality of distributed
sensor nodes is continuously stored into a second sensor data set.

[0108]In a manner substantially similar to block 132, in response to the
input selection corresponding to the target-event, the second sensor data
set is automatically searched for data correlating to the at least one
target-event representative feature using the selected pattern
recognition criteria. In a manner substantially similar to decision block
134, if sensor data correlating to the at least one target-event
representative feature of the target-event is found in the second data
set, the second correlated sensor data is stored. The storage location
may be the same retained data storage used to store representative
features of the first target-event, or another retained data storage.

[0109]Yet another embodiment provides a process that substantially
simultaneously stores correlated sensor data for a plurality of
target-events from a respective plurality of sensor nodes, each node
generating a separate data related to a same or a different sensed
parameter. The manner and method of scaling the computer process 120 for
the parallel and substantially simultaneous storing of correlated sensor
data may be done in any manner known to those in the art.

[0110]Another embodiment includes using the computing power and storage of
a sensor node, such as the sensor node 20 of FIG. 1, to run at least a
portion of the process 120. In conjunction with block 126 of FIG. 5A, the
target even input selection may be preloaded into the sensor node, or may
be communicated to the sensor node over a communications link. Similarly,
in conjunction with block 128, the pattern recognition criteria may also
be preloaded into the sensor node, or may be communicated to the sensor
node over a communications link. At block 136, the retained data storage
that stores the correlated sensor data may be local to the sensor node,
such as the digital storage 25 of FIG. 1. The process 120 includes the
sensor node transmitting at least a portion of the stored correlated
sensor data over a communications link to a central computing device,
such as the central computing device 90 of FIG. 4. The process 120 may
further include deleting the stored sensor data after the data has been
communicated to the central computing device. In an alternative
embodiment, the process 120 includes the sensor node transmitting the
stored correlated sensor data to the central computing device in response
to a pull by the central computing device. In another alternative
embodiment, the process 120 includes the sensor node pushing the stored
correlated sensor data to the central computing device.

[0111]Alternatively, at block 136, the retained data storage may be the
digital storage device 100 of the central computing device 90 of FIG. 4.
The process 120 may include instructions that cause the sensor node to
transmit at least a portion of the found correlated sensor data to the
digital storage device 100 for an initial storage.

[0112]An embodiment includes a communication media embodying the process
120, which, when implemented in a computer, causes the computer to
perform a method. For example, in an embodiment where the process 120 is
implemented in a computing device, such as the computing device 90 of
FIG. 4, instructions embodying the process are typically stored in a
computer readable media, such as without limitation the storage media and
memory of the computing device, and loaded into memory for use.

[0113]A further embodiment includes a method implementing the steps of the
computerized process 120, and a computer readable carrier containing
instructions which, when implemented in a computer, cause the computer to
perform the method of the computer process 120.

[0114]An exemplary system employing certain embodiments described above
may be illustrated by a network system of distributed acoustic sensor
nodes placed on a plurality of city traffic lights. While the
illustrative system describes the networked system as owned by the city
maintaining the traffic lights, the exemplary system may have any
ownership, such as a private, public, and governmental, and may be used
for any purpose, such as private, public, governmental, and military.

[0115]The exemplary system includes an orientation toward gathering and
storing acoustic event data for later identification and retrieval. The
individual nodes may use the power supplied to the traffic light as their
power source, or alternatively, use long-life batteries or solar power.
The individual nodes may communicate with a central computing device by
sending sensor data over the power lines serving the traffic light,
separate wire communication links, or wireless communications links. An
event-data storage program embodying certain embodiments described above
is operating on the central computing device. Depending on the city's
need to accumulate sensor data and total digital data storage space
requirements, a digital storage device within the central computing
device case may be used, or at least one local larger capacity device
proximate to the central computing device may be used.

[0116]In operation of the exemplary system, each sensor node transmits
data related to sensed acoustic data generated by their acoustic sensor
element to the central computing device. While the sensed acoustic data
may be transmitted continuously by each sensor node, optimally in this
embodiment to conserve bandwidth, the data is temporarily stored in the
sensor node and transmitted to the central computing device in batches. A
portion of sensed acoustic data for each sensor node in the network,
including an identification of the originating sensor node, is received
by the event-data storage program operating on the central computing
device and stored in a data set queue in the associated digital storage
device. Optimally, the sensed acoustic data for each sensor node is
stored in a separate data set queue. This illustrative system
contemplates that two things occur before the sensed acoustic data is
received. First, the event-data storage program receive at least one
target-event input selection. Second, a pattern recognition criteria
corresponding to at least one of the representative features of the
target-event be selected. For this exemplary system, the selected
target-events are a gunshot, siren, tire screech, and loud voices. The
event-data storage program automatically searches each sensor data set
for sensor data having representative features correlating to a gunshot,
siren, tire screech, or loud voices using the selected pattern
recognition criteria If sensor data correlating to a representative
feature of a gunshot, siren, tire screech, and loud voices is found, the
program stores the correlated sensor data in a retained data storage. The
retained data storage may have sufficient capacity to archive correlated
event-data for a predetermined time period, such as a week, a month, a
year, or multiple years.

[0117]Optimally, the program also associates and stores a tentative
event-identifier, such as gunshot, siren, tire screech, or loud voices,
with the correlated sensor data. The associated tentative
event-identifier will allow city officials to search the correlated
sensor data by identifying and event from gunshot, siren, tire screech,
or loud voices, and searching the retained data storage by tentative
identifiers instead of what may be a more complicated search use pattern
recognition criteria. After the batch sensed acoustic data is searched,
the program automatically deletes the sensor acoustic data from the data
set queue. The deletion minimizes the amount of digital data storage
necessary in the system by saving only sensor data correlating to
selected target-events.

[0118]While the above exemplary system includes gathering and storing
event-data on a non-real-time basis for later retrieval, an embodiment
allows the system to perform real-time tentative identification of one or
more target-events and save correlating sensor data. For example, sensor
nodes having sufficient computing capacity may be preloaded with one or
more input target-event selections. Each sensor node would automatically
and in substantially real-time search sensor data generated by its local
sensor element for sensor data correlating to the input target-event
selection. Instead of storing for later transmission, the found
correlating sensor data would be immediately transmitted to the central
computing device and be available for use. The data transmission may
include associated tentative event-identifiers. In effect, the sensor
nodes filter their acoustical data and only provide sensor data to the
central computing device that corresponds the inputted target-event
selection. The event-data program may then store the found correlating
sensor data, and notify a user in substantially real-time of receipt of
data having the tentative target identifiers. The notification may be by
a display on a monitor screen coupled with the central computing device.
The user may then listen to the correlated sensor data and take
appropriate action, such as notifying police or fire.

[0119]Another embodiment includes a mobile central computing device that a
user takes into communication range with a network of remote sensor
nodes. A mobile computing device, such as a laptop and a reduced-profile
computing device, provide mobility to the computing device 90. The
mobility allows a user to take the central computing device 90 into the
field and within transmission range of certain sensor nodes of a
distributed network of remote sensor nodes. The sensor nodes typically
have acquired and stored a plurality of sensor data sets, each sensor
data set representing a respective feature sensed by a sensor element of
its respective sensor node. A communication link, typically a wireless
link, is established between the computing device 90 and one or more of
the sensor nodes of the array of sensor nodes 80 of the network of remote
or distributed sensor nodes 70. The user inputs a selection of sensor
data sets to be transmitted from the certain sensor nodes to the
computing device 90. In response, a process running on the computing
device 90 communicates with the one or more sensor nodes, extracts the
sensor data sets, stores them, and provides a confirmation to the user
that the selected sensor data sets have been received. The user typically
will receive the confirmation and move the computing device into
communication proximity to other sensor nodes of the array of sensor
nodes 80. Typically, the stored plurality of sensor data sets are deleted
from the sensor nodes after transmission to the computing device 90 to
free-up storage.

[0120]FIG. 6 illustrates a distributed sensor node event-data archival and
retrieval system 150. The system 150 includes a plurality of distributed
sensor networks, illustrated as first, second, and third distributed
sensor networks 70, 152, and 162 respectively. The distributed sensor
network 70 is described in conjunction with FIG. 4, and the sensor
networks 152 and 162 are substantially similar to the sensor network 70.
Each distributed sensor network includes an array of sensor nodes,
illustrated as a first, second, and third arrays 80, 154, and 164
respectively. Each sensor network also includes at least one central
computing device, illustrated as first, second, and third central
computing devices 90, 156, and 166 respectively, and includes a plurality
of communications links. The arrays of sensor nodes 154 and 164 are
substantially similar to the array of sensor nodes 80 described in
conjunction with FIG. 4. For clarity, only several sensor nodes and their
communications links are illustrated in the arrays 80, 154, and 156 in
FIG. 6.

[0121]The second and third central computing devices 156 and 166 are
substantially similar to the first central computing device 90 of FIG. 4.
The second and third digital data storage devices 158 and 168, and the
associated communications links 159 and 169 that communicate with those
central computing devices are substantially similar to the first digital
data storage device 100 and the first communications link 99, also as
described in conjunction with FIG. 4.

[0122]The system 150 also includes an aggregating computing device 170
that is substantially similar to the central computing device 90 of FIG.
4. The words "central," "aggregating," "collecting," and "archival" are
used in this specification, including the claims, to identify certain
devices and to illustrate a possible network hierarchy environment of one
or more embodiments. These words do not limit the nature or functionality
of a device. The system 150 illustrates a possible network hierarchy
where, in an embodiment, a plurality of central computing devices,
illustrated as the central computing devices 90, 156, and 166, receive
and store sensor node data from a plurality of sensor node arrays,
illustrated as the sensor nodes of the arrays 80, 154, and 164
respectively. The system 150 also illustrates a possible network
hierarchy where, in an embodiment, the aggregating computing device 170
receives and stores, i.e., aggregates, sensor data acquired by a
plurality of central computing devices, illustrated in FIG. 6 as central
computing devices 90, 156, and 166. In another embodiment, the computing
device 170 may function as a central computing device providing sensor
data it received and stored to another aggregating computing device (not
illustrated).

[0123]The computing device 170 communicates with at least one remote
digital data storage device, such as storage devices 100, 158, and 168,
through their associated computing devices 90, 156, and 166,
respectively, using one or more communications links. As illustrated in
FIG. 6, the aggregating computing device 170 also includes communications
ports that allow the computing device to communicate with other devices.
These communications ports are substantially similar to the
communications ports of the computing device 90 of FIG. 4. More
specifically, the computing device 170 includes a sensor communication
port 177 for a wired communication link, such as the wire communication
link 189, providing communications with the central computing device 156
and its associated digital data storage device 158. The computing device
170 also includes a wireless transceiver or receiver coupled with a
communications coupler, such as an antenna 176, for wireless
communication over a communications link, such as a wireless
communication link 186. FIG. 6 illustrates the wireless communication
link 186 coupling the computing device 170 and the computing device 166,
and its associated digital data storage device 168. The computing device
170 further includes a network communications port 178 for wired,
wireless, and/or optical communication over a communication link, such as
the network communications link 188, for communication with a network,
such as a local area network, wide area network, and Internet. FIG. 6
also illustrates a communications link 188 as network link between the
central computing device 90 and its associated digital storage device 99.
The communications link 188 may include an acoustic, radio frequency,
infrared and other wireless connection.

[0124]The system 150 also includes at least one digital storage device as
an event-data archive, illustrated as an archival digital data storage
device 190, which may be substantially similar to the digital data
storage device 100 of FIG. 4. The archival digital storage device 190 may
be a local digital data storage device contained within a case housing
the computing device 170. Alternatively, the archival digital storage
device 190 may be a local and external digital data storage device
proximate to the computing device 170, or it may be remote to the
computing device. The archival digital data storage device 190 is coupled
to the computing device in any event by a communications link 179.

[0125]The aggregating computing device 170 may also have input device(s)
174, such as keyboard, mouse, pen, voice input device, touch input
device, etc. The computing device 170 may further have output device(s)
172, such as a display, speakers, printer, etc. may also be included.
Additionally, the computing device 170 may also have additional features
and/or functionality.

[0126]FIG. 7 is a flow diagram illustrating an exemplary process 200 that
aggregates and stores a plurality of instances of correlated sensor data
in an event-data archive. After a start block, the process 200 moves to
block 202. At block, 202, a plurality of central computing devices, such
as the central computing devices 90, 156, and 166, each transmit a
plurality of instances of correlated sensor data to an aggregating
computing device. The instances of correlated sensor data are typically
acquired by a sensor node operable to sense at least one parameter, and
each instance has been correlated to an event having at least one
representative feature. The instances may be stored in one or more
digital data storage devices, such as the storage devices 100, 158, and
168, associated with the central computing devices 90, 156, and 166,
respectively. In an alternative embodiment, at least one digital data
storage device is remote to its associated computing device. The remote
digital data storage device may be included in one or more sensor nodes.

[0127]In the embodiment illustrated in FIG. 6, the correlated sensor data
is accessed from the storage devices 100, 158, and 168 by their
associated central computing devices 90, 156, and 166, and transmitted
over their associated communications links 108, 186, and 189, to the
aggregating computing device 170. In an embodiment, each instance of the
sensor data was acquired by at least one sensor node of a plurality of
distributed sensor nodes, and each sensor node is part of a network of
sensor nodes. Further, each instance of correlated sensor data may
include an associated tentative event-identifier, which typically is
generated and associated when the instance of correlating sensor data was
found.

[0128]In an alternative embodiment (not illustrated), instances of
correlated sensor data are pulled from the digital data storage devices
in response to a request communicated to their respective central
computing devices by the aggregating computing device 170. In another
embodiment, instances of correlated sensor data are transmitted or pushed
from the digital data storage devices by their associated central
computing device to the aggregating computing device 170.

[0129]At block 204, the plurality of instances of correlated sensor data
are received. At block 206, the plurality of instances of correlated
sensor data are stored in an aggregating digital data storage device,
such as the digital data storage device 190. The aggregating digital data
storage device may be referred to in this specification as an event-data
archive. In an alternative embodiment, the plurality of instances of
sensor data stored in the event-data archive are protected by an
information security measure. Such a protected or secured stored data
arrangement may be referred to in this specification as a "data vault" or
"data lock-box."

[0130]The information security measure typically includes providing at
least one of maintaining information confidentiality, maintaining
information integrity, and limiting access to authorized persons. The
information security measure may be any security measure known to those
skilled in the art, and at a selected level commensurate with the value
of the information contained in the instances of correlated sensor data
and any loss that might accrue from improper use, disclosure, or
degradation. The information security measure may be implemented in
software, hardware, infrastructure, networks, or any other appropriate
manner. In an embodiment, the information security measure may be
associated with the digital data storage device, the plurality of
instances of correlated sensor data, and/or a computing device having a
communication link with the digital data storage device.

[0131]Next, at block 208 the process 200 waits for more event data. If
additional event data is received, the process moves to block 204 and
receives the additional event data. The process 200 then proceeds to the
stop block. In an alternative embodiment, the process 200 includes
deleting at least a portion of the instances of correlated sensor data
from the digital data storage devices 100, 158, and 168 after the
instances have been transmitted to the aggregating computing device.

[0132]The process 200, when implemented in a computing device, causes the
computing device to perform certain steps. For example, in an embodiment
where the process 200 is implemented in a computing device, such as the
aggregating computing device 170 of FIG. 6, the instructions are
typically stored in a computer readable media, such as the storage media
and/or memory of the computing device, and loaded into memory for use. In
certain embodiments, the process 200 aggregates instances of sensor data
correlating to an event from a plurality of remote digital data storage
devices, and stores those instances on a digital data storage device
associated with an aggregating computer as an event-data archive, such as
the archival digital data storage device 190 of FIG. 6.

[0133]FIG. 8 is a flow diagram that illustrates exemplary steps of a
process 220 that searches and retrieves certain instances of stored
correlated sensor data from an event-data archive. After a start block,
the process 220 moves to block 222. At block 222, an input selection is
received from an input-selector corresponding to a target-event having at
least one representative feature. The input-selector may include any
entity, such as a machine, a computing device, and a user.

[0134]The input selection optimally further includes the input-selector
tendering an access authorization, which is used to determine if the
input-selector is a trusted entity. The tendered access authorization may
be by any method or device required by a security measure protecting the
instances of stored sensor data from unauthorized access, such as for
example, a password, and thumb print. For example, a trusted entity may
be a user, machine, or computing device, identified on a list of trusted
parties. For example, the list of trusted parties may include employees
and/or computing devices associated with the owner of the sensor network
system. The tendered access authorization may be the input-selector's
personal identification. Further, a trusted entity may be a member of a
certain class, such as uniformed law enforcement officers, or computing
devices maintained by agencies that employ uniformed law enforcement
officers. For example, uniformed law enforcement officers may include
members of the Federal Bureau of Investigation, Alcohol Tobacco and
Firearms, state patrol, county sheriffs, and local police. Another
example of a trusted party class is a prosecuting attorney, a defense
attorney, and a judicial officer.

[0135]In a less preferred embodiment, the instances of stored sensor data
are not protected by a security measure, and the input selection does not
include tender of an access authorization.

[0136]At block 224, a decision operation determines if the tendered access
authorization establishes the input-selector is a trusted entity and
possesses an access right to the stored correlated sensor data. If the
input-selector is a trusted entity and has an access right, the process
branches to block 226. If the input-selector does not posses an access
right, the process branches to the end block. If a security measure is
not protecting the instances of stored sensor data, then the decision
block 224 is not necessary and the process moves from decision block 222
to block 226.

[0137]At block 226, a pattern recognition criteria is selected
corresponding to at least one representative feature of the target-event.
The criteria is selected in a manner substantially similar to block 128
described in conjunction with FIGS. 5A and 5B, including the alternative
embodiments. At block 228, in response to the input selection
corresponding to the target-event, a plurality of instances of stored
sensor data are automatically searched for data correlating to the
target-event using the selected pattern recognition criteria.

[0138]At decision block 232, a decision operation determines if sensor
data correlating to the at least one target-event representative feature
is found. If the sensor data correlating to the target-event is not
found, the process branches to block 236, where a message equivalent to
"no data found" is provided. If sensor data correlating to the
target-event is found, the process branches to block 234.

[0139]At block 236, the found correlated sensor data is provided. In an
embodiment, the input-selector is the recipient of the correlated sensor
data. In another alternative embodiment, a third party is the recipient
of the correlated sensor data. The third party may include a machine, a
computing device, and a user. In a further embodiment, the input-selector
selects a third party recipient of the correlated sensor data. In an
alternative embodiment, the process at block 222 further includes
receiving an access authorization of the third part tendered by the
input-selector, and the process at decision block 224 further includes
determining if the third party recipient possesses an access right before
providing the correlated sensor data to the third party. The process 220
then moves to the end block.

[0140]In a further alternative embodiment of the process 220, the search
at block 228 proceeds in response to an input-selector designation of a
target tentative-event-identifier. In this embodiment, the received
plurality of instances of correlated sensor data each include an
associated tentative-event-identifier. At block 222, the received
target-event selection includes an input selection corresponding to a
target tentative event-identifier. If a target tentative event-identifier
is selected and no reason exists to search for a representative feature,
the block 226 may be bypassed. At block 228, in response to the input
selection corresponding to the target tentative event-identifier, the
plurality of instances of sensor data are automatically searched for data
correlating to the target tentative event-identifier. If any event data
is found correlating to the target tentative event-identifier at decision
block 232, the found sensor data correlating to the target tentative
event-identifier is provided at block 234.

[0141]The process 220, when implemented in a computing device, causes the
computing device to perform steps. In certain embodiments, the process
220 implements a process that searches and retrieves instances of stored
sensor data from an event-data archive protected by a security measure,
such as the archival digit data storage device 190 coupled to the
computing device 170 of FIG. 6. In other embodiments, the process 220
uses a local computing device to search and retrieve instances of stored
sensor data from remote digital data storage devices, such as the digital
data storage device 168.

[0142]The process 220, when implemented in a computing device, causes the
computing device to perform certain steps. For example, in an embodiment
where the process 220 is implemented in a computing device, such as the
aggregating computing device 170 of FIG. 6, the instructions are
typically stored in a computer readable media, such as the storage media
and/or memory of the computing device, and loaded into memory for use.

[0143]An exemplary system employing certain embodiments described above
may be illustrated by three network systems of distributed sensors, and
an aggregating computing device.

[0144]Referring to FIG. 6, the illustrative exemplary system includes the
previously described exemplary network system of distributed acoustic
sensors placed on city traffic lights as the first sensor network 70, an
exemplary network system of distributed digital image capture devices
located in city parking garages and lots as the second sensor network
152, and an exemplary network of distributed heat/fire thermal sensors
located in city buildings as the third sensor network 162. Each exemplary
sensor network automatically stores correlated sensor data in an
associated retained data storage, such as the digital data storage
devices 100, 158, and 168.

[0145]As with FIG. 4, while the illustrative exemplary system describes
the networked system as owned by the city, the illustrative exemplary
system may have any ownership, such as a private, public, and
governmental, and may be used for any purpose, such as private, public,
governmental, and military. Further, the sensor networks may have
different owners. For example, the first sensor network 70 may be owned
by the city, the second sensor network 152 may be privately owned by a
parking garage operator, and the third sensor network 162 may be
privately owned by a fire alarm company.

[0146]The illustrative exemplary system further includes an aggregating
computing device communications linked to the sensor networks, such as
the aggregating computing device 170 and its archival digital data
storage device 190. The central computing devices of the three networks
transmit the correlated sensor data from their retained data storage to
the aggregating computing device. The aggregating computing device
receives and stores the correlated sensor data from the three networks in
an event-data archive on its associated digital data storage device, such
as device 190. The event-data archive includes a data structure suitable
for later search and retrieval. The event-data archive is subject to an
information security measure that protects the sensor data stored in the
event-data archive from unauthorized access. The security measure is
controlled by the aggregating computing device. The central computing
devices delete the correlated sensor data from their associated retained
data storage after transmission to the aggregating computing device. This
frees storage space for the constant stream of additional correlated
sensor data that is continuously transmitted by sensor nodes of their
respective sensor networks.

[0147]A requesting entity may be an employee or official of an owner or
operator of one of the sensor networks, or may be a potentially
authorized person, machine, network or other entity. A requesting entity
desiring sensor data on an event, such as shooting, enters a gunshot
target-event selection on a user input device of the aggregating
computing device, and tenders an identification number as an access
authorization. In this example, the gunshot (event 6 of FIG. 3) may have
occurred near an intersection controlled by a city traffic light at a
known date.

[0148]An event-data retrieval process operating on the aggregating
computing device receives the target-event selection and the employee
identification number. The process determines that the requesting entity
is a trusted entity and possesses an access right. In response to the
gunshot target-event selection, the event-data retrieval process
automatically selects a pattern recognition criteria corresponding to at
least one representative feature of a gunshot. Then, the event-data
retrieval process in response to the gunshot input selection,
automatically searches the event-data archive for instances of acoustic
sensor data correlating to the at least one representative feature of a
gunshot on the known date. Correlating found instances of archived sensor
data are provided to the requesting entity, or a trusted third party
selected by the requesting entity.

[0149]In further reference to FIG. 8, another embodiment provides a
process that searches and retrieves certain instances of stored
correlated sensor data from an event-data archive. After a start block,
the embodiment includes receiving an input selection from an
input-selector, similar to the process 220 at block 222. The input
selection corresponds to a target-occurrence having a representative
feature. A filter corresponding to the representative feature of the
target-occurrence is selected. A plurality of instances of occurrence
data stored in a data set are filtered for data correlating to the
target-occurrence representative feature a using the selected filter.
Each instance of the stored occurrence data has a representative feature.
An output responsive to the filtering is provided. The process then ends.
The filtering step may further include automatically filtering the data
stored in the data set. In a further embodiment, the output responsive to
the filtering correlates to a target-occurrence representative feature,
which is stored in another data set. Alternatively, in another
embodiment, the output responsive to the filtering does not correlate to
a target-occurrence representative feature. The non-correlating output is
stored in another data set.

[0150]FIG. 9 is a flow diagram illustrating exemplary steps of a process
300 that searches a plurality of instances of event data stored in a data
vault or data lock box and provides an output. Each instance of the event
data has at least one representative feature, is stored in a digital data
storage device, and is protected by an information security measure. The
digital data storage device may be a local digital data storage device or
a remote digital data storage device. The information security measure
may be associated with the digital data storage device, the plurality of
instances of stored event data, and/or a computing device having a
communication link with the digital data storage device. In another
embodiment, the digital data storage device includes a portable digital
data storage device, such as an external hard drive, a DVD, a CD, a
floppy disk, and a flash memory device. In a further embodiment, the
event data includes sensor data generated by a plurality of networked
sensor nodes.

[0151]The process 300 is similar to the process 220. After a start block,
the process 300 moves to block 302. At block 302, an input selection is
received from an input selector, the input selection corresponding to a
target-event having at least one representative feature. The received
input selection further includes an output recipient selection and a
tendered access authorization.

[0152]At block 304, in response to the tendered access authorization, a
decision operation determines if an access right to the plurality of
instances of stored event data protected by the information security
measure is possessed by at least one of the input-selector and the
recipient. If the decision operation determines that either the
input-selector and/or the recipient are a trusted entity and posses an
access right to the instances of stored event data, the process branches
to block 306. If neither the input-selector nor the recipient is a
trusted entity, the process branches to the end block. In an alternative
embodiment, the input-selector and the recipient must each possess an
access right.

[0153]At block 306, a pattern recognition criteria is selected
corresponding to at least one representative feature of the target event.
The criteria is selected in a manner substantially similar to block 128
described in conjunction with FIGS. 5A and 5B, and to block 226 described
in conjunction with FIG. 8, including the alternative embodiments.

[0154]At block 308, in response to the input selection corresponding to
the target event, the plurality of instances of stored event data are
automatically searched for data correlating to the at least one
target-event representative feature using the selected pattern
recognition criteria.

[0155]At decision block 312, a decision operation determines if event data
correlating to the at least one target-event representative feature was
found. If the event data correlating to the target-event representative
feature was not found, the process branches to block 316, where a message
equivalent to "no data found" is provided. If event data correlating to
the target was found, the process branches to block 314. At block 314, an
output indicative of the result of the automatic search at block 308 is
provided to the recipient.

[0156]In a further alternative embodiment of the process 300, the search
at block 308 proceeds in response to an input-selector designation of a
target tentative event-identifier in a substantially similar manner as
the process 200 described in conjunction with FIG. 8.

[0157]The process 300, when implemented in a computing device, causes the
computing device to perform certain steps. For example, in an embodiment
where the process 300 is implemented in a computing device, such as the
aggregating computing device 170 of FIG. 6, the instructions are
typically stored in a computer readable media, such as the storage media
and/or memory of the computing device, and loaded into memory for use.

[0158]FIG. 10 is a flow diagram illustrating exemplary steps of a process
350 providing the output of the block 314 of FIG. 9. The illustrated
embodiment includes a set of possible outputs 360 from the output at
block 314. The set of possible outputs 360 illustrated in FIG. 10
includes a first subset of outputs for event data correlating to the
target-event representative feature, and a second subset of outputs for
event data not correlating to the target-event representative feature,
i.e., non-correlating. The first subset includes a correlating tentative
event-identifier 362, a degraded correlating event-data representation
363, and a correlating event data 364. The second subset includes a
non-correlating tentative event-identifier 366, a degraded
non-correlating event-data representation 367, and a non-correlating
event data 368. The process 350 at block 314 includes a default
configuration, indicated by solid hierarchal lines 361, that provides the
correlating tentative event-identifier 362 and the non-correlating
tentative event-identifier 366. In an alternative embodiment, the output
configuration provides the degraded correlating event-data representation
363 and the degraded non-correlating event-data representation 367. In
another alternative embodiment, the output configuration provides only
the correlating event data 364.

[0159]At block 314, the initial output is provided to the input-selector
and/or recipient in any manner and using any output device, such as being
displayed on a monitor of a computing device. For example, the output may
include displaying a table having columns that include an event data
date, a tentative event identifier, and a correlating/non-correlating
status. Individual instances of the plurality of instances of stored
event data are individually displayed in rows of the table. For example,
in response to a target-event selection of a gunshot, which is event 6 of
FIG. 3, one row may display a date of May 17, 2004, a tentative
event-identifier of a "gunshot," and a status of "correlating." Another
row may display the same date of May 17, 2004, a tentative
event-identifier of "unknown" because no correlation to a representative
feature of a gunshot was found, and a status of "non-correlating." In an
alternative embodiment, the output at block 314 may include a ranking for
at least two instances of the correlating event data in a hierarchy of
the found correlating event data. For example, if the provided output in
the above example includes a plurality of events having "gunshot"
tentative event-identifiers, the provided output may further include a
relative or absolute ranking based on the acoustic intensity of the
respective events as an aid to the recipient in evaluating the event
data.

[0160]At block 322, an event-data selection is received from the
input-selector, who may be the recipient. The selection corresponds to at
least one of the instances of event data provided by the process at block
314 and requests provision of more detail related to the provided
instances. In the default configuration, the input selection may
correspond to a tentative event-identifier. For example, the input
selection may request provision of degraded correlating event data
corresponding to the event of May 17, 2004, and tentatively identified as
gunshot.

[0161]At block 324, the selected event data is provided in a form of
degraded correlating data. In an embodiment, the degraded correlating
event data includes sufficient data for the recipient to make a
preliminary determination whether the event appears to be a gunshot. For
example the recipient may listen to the degraded data or view a display
of a time-frequency analysis of the degraded data. The process 350 then
terminates at the end block.

[0162]If the recipient possesses an access authorization for the
correlating event data 364, the event-data selection may include
receiving another input selection that requests that the correlating
event-data be provided. The process at block 316 receives the another
event-data selection, and at block 318 provides the output. Continuing
with the above example, the recipient may request complete event data
(364) from all the sensors that correlates to the gunshot.

[0163]The process 350, when implemented in a computing device, causes the
computing device to perform certain steps. For example, in an embodiment
where the process 350 is implemented in a computing device, such as the
aggregating computing device 170 of FIG. 6, the instructions are
typically stored in a computer readable media, such as the storage media
and/or memory of the computing device, and loaded into memory for use.

[0164]FIG. 11 is a flow diagram illustrating exemplary steps of a process
400 that redacts a selected instance of event data from the plurality of
instances of stored event data described in conjunction with FIG. 9.
After a start block, the process moves to block 402, where a redaction
selection and a tendered redaction authorization are received. The
redaction selection includes a selection of at least one of the plurality
of instances of event data. In an embodiment, the redaction selection may
be correlated with the provided output at block 314 of FIGS. 9 and 10.
Using the above example where a plurality rows are displayed in a table
on a monitor, individual target-event-identifiers may be hyperlinked.
This allows an input-selector to select an event for redaction by
activating a link in a displayed row.

[0165]At block 404, in response to the tendered redaction authorization, a
decision operation determines if at least one of the input-selector and
the recipient possess a redaction right to the plurality of instances of
stored event data protected by the information security measure. If the
decision operation determines that either the input-selector and/or the
recipient are a trusted entity and posses a redaction right, the process
branches to block 406. If neither the input-selector nor the recipient is
a trusted entity, the process branches to the end block.

[0166]At block 406, the selected event data is redacted from the plurality
of instances of the stored event data. The redacted instance of event
data may or may not correlate to the at least one target-event
representative feature. The process 400 then terminates at the end block.

[0167]The process 400, when implemented in a computing device, causes the
computing device to perform certain steps. For example, in an embodiment
where the process 400 is implemented in a computing device, such as the
aggregating computing device 170 of FIG. 6, the instructions are
typically stored in a computer readable media, such as the storage media
and/or memory of the computing device, and loaded into memory for use.

[0168]An exemplary system employing certain embodiments described in
conjunction with FIGS. 9-11 may be illustrated using the exemplary system
of the three network systems of distributed sensors and the aggregating
computing device previously described in conjunction with FIG. 8.
Continuing with the previous illustration, the event-data archive
associated with the aggregating computing device now contains correlating
event data acquired from the three-network system over time, such as a
year. The gunshot has resulted in litigation, and the litigants request
discovery of correlating event data in the city's data vault, which is
the city's event-data archive protected by a security measure. The city
is willing to provide relevant instances event data to the litigants and
a court, but unwilling to provide other instances of event data based on
proprietary and citizen privacy concerns.

[0169]A trusted person designated by the court and given an access
authorization by the city provides an input selection corresponding to
the gunshot event of May 17, 2004. For example, the trusted person may be
a neutral expert, an expert witness for a party, and a magistrate. The
input selection is received by an archival event-data process described
in conjunction with FIGS. 9-11, and a determination made that the trusted
person acting as an input-selector possesses an access right to the data
vault. In response to the gunshot target-event selection, the archival
event-data retrieval process automatically selects a pattern recognition
criteria corresponding to at least one representative feature of a
gunshot. The archival event-data retrieval process, in response to the
gunshot input selection, automatically searches the event-data archive
for instances of acoustic sensor data correlating to the at least one
representative feature of a gunshot on the known date.

[0170]An initial output indicative of the search result is provided to the
trusted person. In the exemplary embodiment, the default output
configuration described above provides a table displaying the correlating
tentative gunshot-identifiers (362) and the non-correlating tentative
gunshot-identifiers (366) in rows. The trusted person provides an
event-data selection that corresponds to at least one of the instances of
tentative gunshot-identifiers initially provided by the process. For
example, an initial output may indicate that a plurality of sensors
generated acoustical data correlating to at least one representative
feature of a gunshot, and the input selector selects three of these
instances. The event-data selection is received from the input-selector,
and the archival event-data retrieval process provides the trusted person
with the three selected instances of degraded correlating event data
corresponding to the gunshot. The trusted person listens to the three
instances of degraded event data. If the trusted person concludes two of
the three instances of event data relate to the gunshot, the trusted
person then requests and is provided with the two complete event data for
the two instances.

[0171]Another embodiment of the exemplary archival event-data process
provides a redaction whereby the city through a representative, or the
trusted person, may remove certain instances of event data from the
plurality of instances of event data in the city's data vault. The
redacted data vault may then be given to a third party much like a
redacted paper document. Preferably, the city retains a duplicate of
their data vault prior to beginning the redaction process. The process
includes receiving the redaction selection from the trusted party, and a
tender of a redaction authorization. For example, the redaction selection
may be formulated in terms of redacting all event data except for the
three selected instances of event data correlating to a gunshot.
Alternatively, the redaction selection may be inverted to redact only the
three selected instances of event data correlating to a gunshot. Since
redaction involves alteration of data from the data vault, the city may
require a separate redaction right in addition to the access right.

[0172]The process determines that the trusted party possesses a redaction
right. In response to the redaction selection, all but the three
instances of event data are redacted from the data vault. The data vault
and the three selected instances of gunshot data stored therein may be
made accessible to others involved in the litigation.

[0173]It should be appreciated that the particular embodiments described
herein are merely possible implementations of the present disclosure, and
that the present disclosure is not limited to the particular
implementations described herein and shown in the accompanying figures.
For example, in alternate implementations, certain acts need not be
performed in the order described, and may be modified, and/or may be
omitted entirely, depending on the circumstances. Moreover, in various
implementations, the acts described may be implemented by a computer,
controller, processor, programmable device, or any other suitable device,
and may be based on instructions stored on one or more computer-readable
media or otherwise stored or programmed into such devices. In the event
that computer-readable media are used, the computer-readable media can be
any available media that can be accessed by a device to implement the
instructions stored thereon.

[0174]Various methods, systems, and techniques may be described and
implemented in the general context of computer-executable instructions,
such as program modules, executed by one or more processors or other
devices. Generally, program modules include routines, programs, objects,
components, data structures, etc. that perform particular tasks or
implement particular abstract data types. Typically, the functionality of
the program modules may be combined or distributed as desired in various
alternate embodiments. In addition, embodiments of these methods,
systems, and techniques may be stored on or transmitted across some form
of computer readable media.

[0175]It may also be appreciated that there may be little distinction
between hardware and software implementations of aspects of systems and
methods disclosed herein. The use of hardware or software may generally
be a design choice representing cost vs. efficiency tradeoffs, however,
in certain contexts the choice between hardware and software can become
significant. Those having skill in the art will appreciate that there are
various vehicles by which processes, systems, and technologies described
herein can be effected (e.g., hardware, software, firmware, or
combinations thereof), and that a preferred vehicle may vary depending
upon the context in which the processes, systems, and technologies are
deployed. For example, if an implementer determines that speed and
accuracy are paramount, the implementer may opt for a mainly hardware
and/or firmware vehicle. Alternatively, if flexibility is paramount, the
implementer may opt for a mainly software implementation. In still other
implementations, the implementer may opt for some combination of
hardware, software, and/or firmware. Hence, there are several possible
vehicles by which the processes and/or devices and/or other technologies
described herein may be effected, and which may be desired over another
may be a choice dependent upon the context in which the vehicle will be
deployed and the specific concerns (e.g., speed, flexibility, or
predictability) of the implementer, any of which may vary. Those skilled
in the art will recognize that optical aspects of implementations will
typically employ optically-oriented hardware, software, and or firmware.

[0176]Those skilled in the art will recognize that it is common within the
art to describe devices and/or processes in the fashion set forth herein,
and thereafter use standard engineering practices to integrate such
described devices and/or processes into workable systems having the
described functionality. That is, at least a portion of the devices
and/or processes described herein can be developed into a workable system
via a reasonable amount of experimentation.

[0177]The herein described aspects and drawings illustrate different
components contained within, or connected with, different other
components. It is to be understood that such depicted architectures are
merely exemplary, and that in fact many other architectures can be
implemented which achieve the same functionality. In a conceptual sense,
any arrangement of components to achieve the same functionality is
effectively "associated" such that the desired functionality is achieved.
Hence, any two components herein combined to achieve a particular
functionality can be seen as "associated with" each other such that the
desired functionality is achieved, irrespective of architectures or
intermedial components. Likewise, any two components so associated can
also be viewed as being "operably connected" or "operably coupled" (or
"operatively connected," or "operatively coupled") to each other to
achieve the desired functionality, and any two components capable of
being so associated can also be viewed as being "operably couplable" (or
"operatively couplable") to each other to achieve the desired
functionality. Specific examples of operably couplable include but are
not limited to physically mateable and/or physically interacting
components and/or wirelessly interactable and/or wirelessly interacting
components and/or logically interacting and/or logically interactable
components.

[0178]Those skilled in the art will recognize that some aspects of the
embodiments disclosed herein can be implemented in standard integrated
circuits, and also as one or more computer programs running on one or
more computers, and also as one or more software programs running on one
or more processors, and also as firmware, as well as virtually any
combination thereof. It will be further understood that designing the
circuitry and/or writing the code for the software and/or firmware could
be accomplished by a person skilled in the art in light of the teachings
and explanations of this disclosure.

[0179]The foregoing detailed description has set forth various embodiments
of the devices and/or processes via the use of block diagrams,
flowcharts, and/or examples. Insofar as such block diagrams, flowcharts,
and/or examples contain one or more functions and/or operations, it will
be understood by those within the art that each function and/or operation
within such block diagrams, flowcharts, or examples can be implemented,
individually and/or collectively, by a wide range of hardware, software,
firmware, or virtually any combination thereof. For example, in some
embodiments, several portions of the subject matter described herein may
be implemented via Application Specific Integrated Circuits (ASICs),
Field Programmable Gate Arrays (FPGAs), digital signal processors (DSPs),
or other integrated formats. However, those skilled in the art will
recognize that some aspects of the embodiments disclosed herein, in whole
or in part, can be equivalently implemented in standard integrated
circuits, as one or more computer programs running on one or more
computers (e.g., as one or more programs running on one or more computer
systems), as one or more programs running on one or more processors
(e.g., as one or more programs running on one or more microprocessors),
as firmware, or as virtually any combination thereof, and that designing
the circuitry and/or writing the code for the software and or firmware
would be well within the skill of one of skill in the art in light of
this disclosure.

[0180]In addition, those skilled in the art will appreciate that the
mechanisms of the subject matter described herein are capable of being
distributed as a program product in a variety of forms, and that an
illustrative embodiment of the subject matter described herein applies
equally regardless of the particular type of signal bearing media used to
actually carry out the distribution. Examples of a signal bearing media
include, but are not limited to, the following: recordable type media
such as floppy disks, hard disk drives, CD ROMs, digital tape, and
computer memory; and transmission type media such as digital and analog
communication links using TDM or IP based communication links (e.g.,
packet links).

[0181]While particular aspects of the present subject matter described
herein have been shown and described, it will be apparent to those
skilled in the art that, based upon the teachings herein, changes and
modifications may be made without departing from the subject matter
described herein and its broader aspects and, therefore, the appended
claims are to encompass within their scope all such changes and
modifications as are within the true spirit and scope of this subject
matter described herein. Furthermore, it is to be understood that the
invention is defined by the appended claims. It will be understood by
those within the art that, in general, terms used herein, and especially
in the appended claims (e.g., bodies of the appended claims) are
generally intended as "open" terms (e.g., the term "including" should be
interpreted as "including but not limited to," the term "having" should
be interpreted as "having at least," the term "includes" should be
interpreted as "includes but is not limited to," etc.). It will be
further understood by those within the art that if a specific number of
an introduced claim recitation is intended, such an intent will be
explicitly recited in the claim, and in the absence of such recitation no
such intent is present. For example, as an aid to understanding, the
following appended claims may contain usage of the introductory phrases
"at least one" and "one or more" to introduce claim recitations. However,
the use of such phrases should not be construed to imply that the
introduction of a claim recitation by the indefinite articles "a" or "an"
limits any particular claim containing such introduced claim recitation
to inventions containing only one such recitation, even when the same
claim includes the introductory phrases "one or more" or "at least one"
and indefinite articles such as "a" or "an" (e.g., "a" and/or "an" should
typically be interpreted to mean "at least one" or "one or more"); the
same holds true for the use of definite articles used to introduce claim
recitations. In addition, even if a specific number of an introduced
claim recitation is explicitly recited, those skilled in the art will
recognize that such recitation should typically be interpreted to mean at
least the recited number (e.g., the bare recitation of "two recitations,"
without other modifiers, typically means at least two recitations, or two
or more recitations). Furthermore, in those instances where a convention
analogous to "at least one of A, B, and C, etc." is used, in general such
a construction is intended in the sense one having skill in the art would
understand the convention (e.g., "a system having at least one of A, B,
and C" would include but not be limited to systems that have A alone, B
alone, C alone, A and B together, A and C together, B and C together,
and/or A, B, and C together, etc.). In those instances where a convention
analogous to "at least one of A, B, or C, etc." used, in general such a
construction is intended in the sense one having skill in the art would
understand the convention (e.g., "a system having at least one of A, B,
or C" would include but not be limited to systems that have A alone, B
alone, C alone, A and B together, A and C together, B and C together,
and/or A, B, and C together, etc.).

[0182]As a further example of "open" terms in the present specification
and claims, it will be understood that usage of a language construction
"A or B" is generally interpreted as a non-exclusive "open term" meaning:
A alone, B alone, and/or A and B together.

[0183]Although various features have been described in considerable detail
with reference to certain preferred embodiments, other embodiments are
possible. Therefore, the spirit or scope of the appended claims should
not be limited to the description of the embodiments contained herein.